Fukuoka Institute of Technology Repository >
02.University Bulletins & Report >
null >
Vol.1 >
|
Title | : | Echo state network による時系列からの非定常性の検出 |
Authors | : | 山口 裕 兒嶋 大也 宮嵜 鋼 |
Issue Date | : | Dec-2018 |
Abstract | : | We developed a novel method that detecting nonstationarity in observed time-series. Using reservoir computing
approach, which now becomes a popular tool in machine learning field, we estimated cross-prediction errors
between different segments of time-series. These errors are considered as a measure of non-similarities between
different segments. Then, multidimensional scaling was used to obtain a reduced representation of relational
patterns among segments. |
Type Local | : | 紀要論文 |
ISSN | : | 24345725 |
Publisher | : | 福岡工業大学総合研究機構 |
Comment | : | 情報科学研究所(Computer Science Laboratory) |
URI | : | http://hdl.handle.net/11478/1219 |
citation | : | 福岡工業大学総合研究機構研究所所報 1 43 46 |
Citation | : | 福岡工業大学総合研究機構研究所所報 Vol.1 p.43 -46 |
Appears in Collections | : | Vol.1
|
Files in This Item:
File |
Description |
Size | Format |
11478-1219_p43.pdf | | 790Kb | Adobe PDF | View/Open |
|
|
|